SubjectsSubjects(version: 845)
Course, academic year 2018/2019
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Machine Learning - NAIL029
Title in English: Strojové učení
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2015
Semester: summer
E-Credits: 3
Hours per week, examination: summer s.:2/0 Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Guarantor: Mgr. Marta Vomlelová, Ph.D.
Class: Informatika Mgr. - Teoretická informatika
Informatika Mgr. - Matematická lingvistika
Classification: Informatics > Theoretical Computer Science
Annotation -
Last update: T_KTI (03.05.2012)
The aim of the course is to introduce machine learning as important and in this time very vital field developing in the close connection with artificial intelligence. The course gives a survey of basic branches of machine learning (supervised inductive learning, reinforcement learning, unsupervised learning and knowledge in learning), main problems and methods and some typical algorithms.
Aim of the course - Czech
Last update: T_KTI (03.05.2012)

Naučit teorii, metody a algoritmy používané ve strojovém učení.

Course completion requirements -
Last update: Mgr. Marta Vomlelová, Ph.D. (07.06.2019)

The exam consists of a written preparation and an oral part. The requirements are given by the course syllabus.

Literature -
Last update: Mgr. Marta Vomlelová, Ph.D. (20.04.2016)

T. Hastie, R. Tibshirani, J. Friedman: The Elements of Statistical Learning, Springer 2009

G. James, D. Witten, T. Hastie, R. Tibshirani: An Introduction to Statistical learning with Applications in R, Springer, 2014

S.J. Russell, P. Norvig: Artificial Intelligence: A Modern Approach; Prentice Hall, 1995

I.H.Witten and E.Frank. Data Mining - Practical machine learning tools and techniques with Java implementation. Academic Press Pub., USA, 1999

Requirements to the exam -
Last update: Mgr. Marta Vomlelová, Ph.D. (07.06.2019)

The exam consists of a written preparation and an oral part. The requirements are given by the course syllabus.

Syllabus -
Last update: Mgr. Marta Vomlelová, Ph.D. (20.04.2016)

Linear regression and instance based learning as "extremal points" in the space of models,

the curse of dimensionality, bias-variance tradeoff,

logistic regression, generalized additive models,

model assessment (confidence intervals, crossvalidation, one-leave-out)

decision trees, prunning, missing values, random forest,

rule search PRIM,

model averaging, boosting, random forest,

support vector machines,

Bayesian learning, EM algorithm introduced on an clustering example,

unsupervised learning - market basket analysis, clustering k-means, k-medoids, hierarchical clustering,

inductive logical programming.

 
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